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Instructors: Marten Borchers
Event type:
Internship seminar
Displayed in timetable as:
Smart City HH
Hours per week:
4
Credits:
6,0
Language of instruction:
German/English
Min. | Max. participants:
- | 24
Comments/contents:
Smart City describes intelligent and innovative cities in which the focus is on citizens, and the aim is to achieve community coexistence with a high quality of life and sustainable use of resources. Smart Mobility focuses in particular on solutions that affect and are intended to improve the mobility of citizens. This is especially done in the context of the transformation towards sustainable and environmentally friendly mobility.
In the internship seminar Data-driven Solutions for the Smart City Hamburg (D2S2C Hamburg), groups of 4 to 6 students analyze different use cases from the field of smart mobility and implement prototype solutions. To enable a high degree of practical relevance, a comparable environment to later working environments, and working with real data and requirements, we cooperate with Hamburger Hochbahn AG (HOCHBAHN). All use cases and solutions are related to data of different kinds and solutions are developed with the help of artificial intelligence (AI) systems and may include AI or machine learning (ML) models. The use cases will be worked on in groups, according to agile project management, which will be introduced and repeated at the beginning of the course. The prototypes are usually developed as (web) prototypes in Python (flask) or Java, although other programming languages are not excluded and can be used depending on the available skills. In addition, methods for data analysis, visualization, and machine learning will be taught and used as needed.
The internship seminar is suitable for all students of the MIN faculty and is especially aimed at students who want to work with a company in a practical way to develop prototypes to solve real challenges.
There are no prerequisites. However, prior knowledge of the web, software development, project management, GitLab, data science, and machine learning is helpful. By working in groups of four to six students, it is also possible to specifically match students with experience in web development with those interested in Data Science and Machine Learning.
The practical seminar will be conducted in German or English as required to enable students from English-speaking degree programs to participate.
Learning objectives:
The didactic approach corresponds to that of inquiry-based learning so that students have a high degree of influence on the content and can follow their intrinsic interests and contribute to individual competencies. In the course of the internship seminar, the acquisition and deepening of the following competencies are aimed.
Core competencies
- Application development in groups (different languages possible, e.g. scripts, local or web-based applications)
- Methods for collecting data & information and dealing with incomplete, compromised, or outdated data
- Methods for the analysis and visualization of data (Data Science)
- Artificial Intelligence (AI) and Machine Learning (ML) in Python or Java (DL4J) e.g. classification and analysis of content
- Social skills and group work
- (Self) organization
Further competencies
- Corporate communications
- IT-supported project management
- Presentation of results
Didactic concept:
The implementation of the internship seminar takes place hybrid. In the first two to four weeks, content from the areas of smart city, smart mobility, project management, web, software development, data science, and machine learning is repeated on-site, and the cooperation partners and use cases to be worked on are introduced.
In week two, the group work begins to address the use cases, identify challenges, and outline solutions. The group work is accompanied by the teachers and there are regular meetings with the cooperation partners to discuss content, ask questions, and evaluate prototypes. After 2/3 of the semester, the intermediate results are presented and after the end of the lecture period, the results of the groups are presented in the plenum.
In the past, different data sources and frameworks were also used for development. This concerns e.g. OpenStreetMap, the GoogleMaps API, weather databases, or machine learning models that can be used via Google Clouds or also Open AI (Chat-GPT as an assistance system and already trained ML models). Corresponding accesses can be provided as needed and in consultation.
The internship seminar starts on 3rd April at 08:30 and ends at 11:45 (incl. a break). In agreement with all the students, the time can be changed to e.g. 09:00 to 12:00. This will be discussed and decided in the first appointment.
Literature:
Relevant literature will be provided at the beginning of the course.
Additional examination information:
The seminar will be graded, and the following performances are crucial.
- Active participation and independent work
- Interim presentation of results
- Presentation of the final results of the group work
- Final report on the results including developed prototypes or similar
The lecture conditions will be presented and discussed at the beginning of the session.
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